Facilitating a meeting using graphical text analysis

ABSTRACT

Embodiments relate to facilitating a meeting. A method for facilitating a meeting of a group of participants is provided. The method generates a graph of words from speeches of the participants as the words are received from the participants. The method partitions the group of participants into a plurality of subgroups of participants. The method performs a graphical text analysis on the graph to identify a cognitive state for each participant and a cognitive state for each subgroup of participants. The method informs at least one of the participants about the identified cognitive state of a participant or a subgroup of participants.

BACKGROUND

The present invention relates generally to facilitating a meeting, andmore specifically, to facilitating a meeting based on a graphical textanalysis of the speeches by the meeting participants.

In a meeting, the participants may go through different states of mindor cognitive states depending on the course of the conversation thattakes place among the participants. These cognitive states may includeinterests, current knowledge, goals, desires, emotional states (e.g.,anger, frustration, irritation, happiness, satisfaction, etc.), to namea few. These cognitive states may be revealed implicitly or explicitlythrough the words that the participants speak during the meeting. Forinstance, in a classroom setting, the words that a student utters mayshow cognitive development of the student in the context of the lessonthat a teacher is presenting.

SUMMARY

Embodiments include a computer program product, a method, and a systemfor facilitating a meeting. According to an embodiment of the presentinvention, a computer program product is provided. The computer programproduct comprises a computer readable storage medium having programinstructions embodied therewith. The program instructions readable by aprocessing circuit cause the processing circuit to perform a method offacilitating a meeting of a group of participants. The method generatesa graph of words from speeches of the participants as the words arereceived from the participants. The method partitions the group ofparticipants into a plurality of subgroups of participants. The methodperforms a graphical text analysis on the graph to identify a cognitivestate for each participant and a cognitive state for each subgroup ofparticipants. The method informs at least one of the participants aboutthe identified cognitive state of a participant or a subgroup ofparticipants.

According to another embodiment of the present invention, a method forfacilitating a meeting of a group of participants is provided. Themethod generates a graph of words from speeches of the participants asthe words are received from the participants. The method partitions thegroup of participants into a plurality of subgroups of participants. Themethod performs a graphical text analysis on the graph to identify acognitive state for each participant and a cognitive state for eachsubgroup of participants. The method informs at least one of theparticipants about the identified cognitive state of a participant or asubgroup of participants.

According to a further embodiment of the present invention, a computersystem for facilitating a meeting of a group of participants isprovided. The computer system comprises a memory having computerreadable instructions and a processor configured to execute the computerreadable instructions. The instructions comprise generating a graph ofwords from speeches of the participants as the words are received fromthe participants. The instructions further comprise partitioning thegroup of participants into a plurality of subgroups of participants. Theinstructions further comprise performing a graphical text analysis onthe graph to identify a cognitive state for each participant and acognitive state for each subgroup of participants. The instructionsfurther comprise informing at least one of the participants about theidentified cognitive state of a participant or a subgroup ofparticipants.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as embodiments is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe embodiments are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 4 depicts a system for facilitating a meeting according to anembodiment of the invention;

FIG. 5 depicts a graph of words according to an embodiment of theinvention;

FIG. 6 depicts a graph of words according to an embodiment of theinvention; and

FIG. 7 depicts a process flow for facilitating a meeting according to anembodiment of the invention.

DETAILED DESCRIPTION

In some embodiments of the invention, the systems and methods perform agraphical text analysis on the speeches of a group of participants in ameeting to identify the cognitive states of the participants and toidentify transitions between different cognitive states of theparticipants during the meeting in real time. More specifically, thesystems and methods partition the group into a plurality of subgroups ofparticipants. The systems and methods then perform the graphical textanalysis on the speeches of each subgroup so as to identify meetingdynamics between different subgroups based on the cognitive states andthe transitions between the cognitive states for the subgroups. In someembodiments, the systems and methods generate an advice based on themeeting dynamics and present the advice to the participants of themeeting so as to influence the course of the meeting.

A meeting, as used in this disclosure, refers to a conversation carriedout by two or more participants. A meeting may take placeface-to-face—all participants are gathered at the same physical locationand talk to each other, with the aid of microphones and speakers ifnecessary. In some cases, a meeting may take place remotely over anetwork when some or all participants in a meeting are remotely locatedfrom each other. That is, the participants carry on a conversationthrough a network by exchanging audiovisual and/or textual informationusing video/audio teleconference facilities or text messaging systems.Moreover, the meeting participants in a meeting have a variety ofdifferent relationships. For instance, one of the participants may be ateacher and the rest may be the students, or some of the participantsmay be managers and the rest are the subordinates.

A meeting participant may seek information, help, guidance,instructions, etc. in a meeting. The participant may seek knowledge butalso desire to express an opinion, provide feedback, and/or receive aresponse with a certain degree of empathy, understanding, speed,terseness, etc.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and meeting facilitation and graphical text analysis.

FIG. 4 illustrates a system 400 for facilitating a meeting. In someembodiments, the system 400 includes modules, sub-modules and datastoressuch as a text generation module 402, a text receiving module 404, agraph constructing module 406, a meeting partitioning module 410, agraphical text analyzing module 412, a transition detecting module 414,an advising module 416, an external information processing module 418, aclusters repository 420, a graphs repository 422, a cognitive statesrepository 424, and an external information repository 426. FIG. 4 alsoillustrates participants 480 and other external information sources 482.

The text generation module 402 transcribes the speeches into text.Specifically, the text generation module 402 converts the audio signalsof the speeches of the participants 480 into text. The text generationmodule 402 may use one or more known speech-to-text techniques forconverting audio signals of the speeches into text. The audio signalsmay be received from remote participants via a network such as theInternet (not shown) or from one or more microphones (not shown) used bylocal participants to participate in the meeting.

The text receiving module 404 receives the speeches of the participantsin text. The text of the speeches may be received from remote devices(i.e., stationary and mobile computers such as desktops, mobilecomputers, and smartphones, etc.) that the participants use toparticipate in the meeting. These devices may convert the audio signalsof the speeches into the text before sending the text to the system 400or provide textual communication tools (e.g., side-chats, instantmessaging, emails, tweets, TTY phone calls, faxes, social media content,etc.) for the participants to use to participate in the meeting.

The graph constructing module 406 receives the text of the participants'speeches from the text generation module 402 and/or the text receivingmodule 404. The graph constructing module 406 then builds a graph fromthe received text for each meeting participant. In some embodiments, thegraph constructing module 406 builds one graph that includes words ofthe all participants' speeches. Alternatively or conjunctively, thegraph constructing module 406 builds one graph for each participant thatincludes only the words of the participant's speech.

More specifically, in some embodiments, the graph constructing module406 extracts syntactic features from the received text and converts theextracted features to vectors. These syntactic vectors can have binarycomponents for the syntactic categories such as verb, noun, pronoun,adjective, lexical root, etc. For instance, a vector [0,1,0,0 . . . ]represents a noun-word in some embodiments.

The graph constructing module 406 may also generate semantic vectorsfrom the received text using one or more known techniques (e.g., LatentSemantic Analysis and WordNet). The semantic content of each word in thetext may be represented by a vector, of which the components aredetermined by Singular Value Decomposition of word co-occurrencefrequencies over a large database of documents.

In some embodiments, the graph constructing module 406 may also usepreviously generated or received text (e.g., transcripts from previousmeetings) to generate the graphs. In these embodiments, the graphconstructing module 406 may apply smaller weighting factors to the textgenerated prior to the current meeting. The graph constructing module406 applies smaller weights to text from older meetings.

A graph generated for a meeting participant by the graph constructingmodule 406 may be in the form of: G={N, E, {hacek over (W)}}, where thenodes N represent tokens (e.g., words or phrases), the edges E representtemporal precedence in the participant′ speech, and each node possessesa feature vector {hacek over (W)} defined in some embodiments as adirect sum of the syntactic and semantic vectors and additionalnon-textual feature vector (e.g., a predetermined vector for theidentity of the participant). That is, in some embodiments, the featurevector {hacek over (W)} is defined by the equation: {hacek over(W)}={hacek over (w)}_(sym)⊕{hacek over (w)}_(sem)α{hacek over(w)}_(ntxt), where {hacek over (W)} is the feature vector, {hacek over(w)}_(sym) is the syntactic vector, {hacek over (w)}_(sem) is thesemantic vector, and {hacek over (w)}_(ntxt) is the non-textualfeatures. An example graph 500 that may be generated by the graphconstructing module 406 is shown in FIG. 5. As shown, the graph 500 is adirected graph that includes an ordered set of words or phrases, eachwith a feature vector. Loops may form in this graph if the same words orphrases are spoken more than once.

Referring back to FIG. 4, the graph constructing module 406 updates thegraph as more text from the meeting participant is received from thetext generation module 402 and/or the text receiving module 404 duringthe meeting. The graph constructing module 406 stores the generatedgraphs for the participants in the graphs repository 422.

The graphical text analyzing module 412 uses one or more machinelearning tools and techniques known in the art to extract topologicalfeatures from the graph and makes inferences about the cognitive stateof each participant. The graphical text analyzing module 412 performs agraphical text analysis on the graph for each meeting participantgenerated by the graph constructing module 406. As a specific example ofgraphical text analysis, in some embodiments, the graphical textanalyzing module 412 analyzes the graph G for the participant generatedby the graph construction module 408 based on a variety of features. Thevariety of features include known graph-theoretical topological measuresof the graph skeleton (i.e., a graph without features vectors:G_(Sk)={N, E}) such as degree distribution, density of small-sizemotifs, clustering, centrality, etc. Similarly, additional values may beextracted by including the features vectors for each node of the graph.One such instance is the magnetization of the generalized Potts model(e.g., H=Σ_(n)E_(nm){hacek over (W)}_(n)

{hacek over (W)}_(m)) such that temporal proximity (e.g., number ofedges between two nodes) and feature similarity are taken into account.These features, which incorporate the syntactic, semantic and dynamicalcomponents of the speech, are then combined as a multi-dimensionalfeatures vector {hacek over (F)} that represents a speech sample. Thisfeature vector is finally used to train a standard classifier:M=M({hacek over (F)}_(train),C_(train)), to discriminate speech samplesthat belong to different conditions C, such that for each text speechsample the classifier estimates its condition identity based on theextracted features: C (sample)=M({hacek over (F)}_(sample)).

In some embodiments, the graphical text analyzing module 412 comparesthe graph with the clusters of previously generated graphs stored in theclusters repository 420. More specifically, the feature vectors ofpreviously generated graphs with known cognitive states are plotted in amulti-dimensional text feature space to form clusters in that space. Thegraphical text analyzing module 412 plots the feature vectors of thegraph for the participant in the space in order to determine whether thegraph belongs to a cluster based on, e.g., distance between the plots ofthe graph and the plots of the clusters. If the plots of the graph fallin the feature space of a particular cluster, the correspondingcognitive state represented by the cluster is determined as thecognitive state of the participant.

The graphical text analyzing module 412 stores the identified cognitivestate of the participant in the cognitive states repository 424. Thegraphical text analyzing module 412 determines like-mindedness amongmeeting participants based on the identified cognitive states. In someembodiments, the cognitive states repository 424 stores a history ofcognitive states for each meeting participant.

The meeting partitioning module 410 partitions the group of participantsin the meeting into a plurality of subgroups that each include one ormore participants. Specifically, in some embodiments, the meetingpartitioning module 410 partitions the group into all possible numbersof subgroups—e.g., into two subgroups, three subgroups, and up to Msubgroups, where M is the number of the meeting participants. Thenumbers of participants in the subgroups do not have to match—differentsubgroups may include different number of participants.

The different subgroups may have different relationship with each other.For instance, one subgroup may include teachers and other subgroup(s)may include students. Also, one of the subgroups may be a group ofmeeting facilitators or helpers and other subgroups may be participantsof two sides of a debate.

The participants within a subgroup may also have different relationshipswith each other. The meeting participant may also be an individualteamed with a helper artificial agent with natural language processingcapabilities so that a subgroup including the helper artificial agentand a participant form a composite participant. A participant may alsobe an artificial agent that acts on behalf of a human participant. Thus,in this case, the analysis is being performed on linguistic output of anartificial agent who may participate in a meeting or help a humanparticipant. In some embodiments, the meeting partitioning module 410may use known techniques to identify the participants and associate theparticipant identity with a speech.

The meeting partitioning module 410 also generates a composite graph foreach of the subgroups of participants by merging the graphs forindividual participants in the subgroup or by partitioning the graph forall participants into a plurality of subgraphs that each corresponds toa subgroup of participants. In some embodiments, each node of acomposite graph generated for a subgroup of participants indicates whois the speaker of the words or phrases of the node. The order of theconversation may be captured and preserved in the composite graphs. Themeeting partitioning module 410 stores the composite graphs for thesubgroups in the graphs repository 426. FIG. 6 illustrates an examplegraph 600 for a subgroup of three participants. As described above, thenodes for different participants may include information about theidentities of the participants. Specifically, the nodes for aparticipant are depicted in black, the nodes for another participant aredepicted in white, and the nodes for yet another participant aredepicted in grey.

Referring back to FIG. 4, the graphical text analyzing module 412performs the same graphical text analysis on each composite graph toidentify a cognitive state of the participants in the subgroup. Eachsubgroup of participants therefore represents a super-organism, a hivemind, or a composite person that is reflective of more than oneparticipant. In some cases, a super-organism may take the form of ameeting participant (e.g., a student) and a meeting leader dyad (e.g., ateacher), for which the participant and the leader are analyzed as oneunit. Based on such an analysis of the super-organism dyad, thegraphical text analyzing module 412 may make additional inferences. Theexample graph 600 illustrated in FIG. 6 may also represent threesubgroups of participants or three composite persons in someembodiments. That is, the nodes for one composite person are depicted inblack, the nodes for another composite person are depicted in white, andthe nodes for yet another composite person are depicted in grey. Inthese embodiments, each node of the graph may also include informationabout identities of the composite persons in addition to or alternativeto the individual identifies of the participants.

In some embodiments, the graphical text analyzing module 412 may givedifferent weights to different participants in a subgroup. For instance,a primary person on a teleconference (e.g., a meeting leader or simplyany person in the meeting) may receive a bigger weight than his or hercolleagues in the meeting. The weights may depend on various factorssuch as a person's position in a company, a measure of networkconnectivity in a social network, etc. These factors may be retrievedfrom the external information repository 425.

The transition detecting module 414 monitors the cognitive states of theindividual participants or the subgroups of the participants to detect atransition from one cognitive state to another as the meetingprogresses. In some embodiments, the transition detecting module 414 mayalso predict the cognitive state by building statistical models toidentify likely transitions of cognitive states, indicating howindividual participants are participating in the meeting in real time.For instance, the transition detecting module 414 may build a Markovmodel with probabilities of a transition and uses the model tocharacterize the transition based on the topologies of the graphs orcomposite graphs.

The advising module 416 automatically generates an advice to present tothe individual participants or the subgroups of the participants basedon the cognitive states or transition detected or predicted by thetransition detecting module 414 in order to influence the course ofmeeting. The advising module 416 may alert each participant or subgroupabout the cognitive state (e.g., detect growing levels ofirritability/anger and issue an alert) and suggest corrective actions toimprove the course of the conversation. The advising module 416 may alsodetermine that the meeting is being diverged based on the cognitivestate transitions for different participants or subgroups. Thus, thefunctioning of the system 400, a computing device, may be improved.

The advising module 416 generates an advice indicating the divergenceand presents to the participants or the subgroups so that theparticipants may recognize the divergence and take the advice to adjustthe course of the meeting. For instance, in a learning environment, whenthe transitions of the cognitive states for the students indicate thatthe students do not understand the lessons being provided by theteacher, advice may be generated and presented to the teacher so thatthe teacher recognizes the effectiveness of the teacher's communicationand adjusts the teaching. The advice may be individually presented tothe students whose cognitive states diverge.

The advising module 416 generates an advice based on the externalinformation stored in the external information repository 424. Forinstance, the digital lesson plans for the teacher are compared againstcognitive state transition models to establish correlations betweenlessons and cognitive state changes. The advice may also relate toguidance on how a person or team may present information in terms ofsuch aspects as speed of presentation, vocal characteristics, word use,emotionality, etc. In some embodiments, the advising module 416 usesindividual cast in a secure voting and this voting may be used ingenerating a proper advice, feedback, warning to the participant.

In some embodiments, the advising may be done via messages conveyed on ahead-mounted display (e.g., Google Glass), screens, earpieces, reportsto participants, reports to others (e.g., meeting facilitators), etc. Insome embodiments, an artificial agent with natural language processingcapabilities (e.g., IBM's DeepQA system) may be used to present theadvice. A real or virtual agent may be represented as an avatar in avirtual world, and the decision to switch to, or make use of, a virtualworld setting may be depended on the analyses performed.

The advising module 416 may suggest helper teachers or alternativeteachers. The alternative teacher may be any of a human, a team ofhumans, a question and answer (Q&A) artificial agent (e.g., IBM's DeepQAsystem) with useful natural language processing abilities, etc. In somesense, the advising module 416 widens the applicability of (and serve asa “front end” to) a Q&A artificial agent. For example, the advisingmodule 416 enhances such Q&A responses, so that the information oranswers provided by the Q&A artificial agent have higher value. If thealternative teacher (or helper teacher) is a Q&A artificial agent,information may be emotively conveyed to a user as useful. For example,the responses may be transformed into data that additionally representsa simulated emotional state and potentially transmitted using an avatarin a virtual world. Data representing an avatar that expresses thesimulated emotional state may be generated and displayed.

For such Q&A artificial agents, the external information processingmodule 418 enhances the interpretation of natural language utterances.The external information processing module 418 may be distributed over aclient and server architecture (i.e., the client being participantdevices), so that the scope of emotion recognition processing tasks maybe allocated on a dynamic basis based on processing resources,communication channel conditions, loads on the client, etc. In somecases, partially processed prosodic data may be sent separately orcombined with other speech data from the client device and streamed tothe advising module 416 via the external information processing module418 for a real-time response. In some embodiments, the externalinformation processing module 418 is trained with real world expectedresponses to improve emotion modeling and the real time identificationof potential features such as emphasis, intent, attitude and semanticmeaning in the participant's utterances.

The external information processing module 418 also gathers andprocesses non-textual information to facilitate the identification ofthe cognitive states for the individual participants or the subgroups ofthe participants. For instance, the external information processingmodule 418 receives biometric information or physiological measurements(e.g., heart rate variability, blood pressure, galvanic skinconductance, electroencephalography, facial expressions, etc.) from theparticipants. The external information processing module 418 analyzesthe biometric information to better identify the cognitive state orboost the confidence in the prediction of a transition from onecognitive state to another when the model built by the transitiondetecting module 414 shows that a transition is predicted with aconfidence level or a likelihood that is smaller than a threshold.

In some embodiments, the advising module 416 automatically performsother confidence enhancing actions based on the external informationstored in the external information repository 424. In some cases, thecognitive state of a student (e.g., someone seeking help of a teacher)is identified with a confidence level or a likelihood. When the value ofconfidence level is below a threshold, the advising module 416 does notinitiate a change in teacher (or getting help from an extra teacher) orchange in the lesson plans. However, if the value of confidence level isgreater than the threshold, the advising module 416 automaticallyinitiates a change in teacher (or obtains a helper teacher) or change inthe lesson plans.

If the confidence level is less than the threshold, the advising module416 automatically performs a confidence increasing action based on theexternal information stored in the external information repository 424,such as an analysis of other student in a student's social network(e.g., people close to the student), an analysis of prior fragments oftext and/or speech of the student (e.g., a person seeking help), ananalysis of a prior fragments of text and/or speech of individuals inthe student's social network, etc. that are gathered and processed bythe external information processing module 418. The advising module 416assigns various weights to the prior fragment. For example, the furtherinto the past a fragment occurs, the lower the weights of such fragmentsthe advising module 416 assigns.

The advising module 416 automatically performs other confidenceincreasing actions if the confidence level is smaller than thethreshold. The advising module 416 may use more public informationobtained about a student, for example, posts made in a social mediaaccount (e.g., Facebook), various public communications, an analysis ofpast help queries, demographic information associated with the student,etc. gathered from the external information sources 482 and processed bythe external information processing module 418. The use of suchinformation may be approved in an opt-in fashion so that a student givespermission to perform such analyses if he or she wishes to receivebetter help, Another confidence increasing action that the advisingmodule 416 may perform is to query the student regarding whether it isestimating the client's psychological state appropriately or correctly.For example, if the teacher is an artificial Q&A agent, it may ask thestudent if he or she is confused or angry, in order to increase thevalue of confidence level.

The external information processing module 418 also monitors andcaptures real-time participant events and stimuli in a learningenvironment. The participant events include classroom andcollaboration-oriented input. The external information repository 418stores a-priori skills of a typical student and a-priori knowledge of aparticipating student. In some embodiments, the external informationprocessing module 418 receives and stores a set of cognitive modelprofiles in the external information repository 424 representing typicalstudent behaviors and participant student behaviors with access to thecaptured participant events, the stimuli and the a-priori knowledge andskills. The advising module 416 uses such additional information.

The external information processing module 418 may be configured to beresponsive to the participant events and stimuli to perform interactivetasks during a class session. The interactive tasks may include posing aquestion, supplementing a lecture, tracking progress and rating teacherperformance. The external information processing module 418 also storesmany questions based on various teaching parameters in the externalinformation repository 424 and creates and displays a feedback formwhich is filled by the students. Each of the questions in the feedbackform is assigned a predetermined weight and pertains to at least oneteaching parameter. This weight enables the system to compute a scorefor teachers teaching parameter-wise, department-wise or educationalinstitution-wise, and these teachers may be Q&A agents. Such reports maybe used by the advising module 416 in conjunction with the graphicaltext analysis described above. The students may be composite students ordyads described above.

As used herein, the terms module and sub-module may refer to anapplication specific integrated circuit, an electronic circuit, aprocessor (shared, dedicated, or group) and memory that executes one ormore software or firmware programs, or a combinational logic circuit ina server. For example, in some embodiments, the text generation module402 may be communicatively connected (e.g., through a bus 456) to amemory 452 to store and retrieve the audio signals and the textconverted from the audio signals, and to a network interface 454 tocommunicate with the devices that the participants 480 use toparticipate in the meeting. The external information processing module418 also uses the network interface 454 to communicate with the externalinformation sources 482 and the participants 480. The graph constructingmodule 406 may use a processor 458 to construct graphs from the text.The graphical text analyzing module 412 may also use the processor 458to perform a graphical text analysis on the graphs. Moreover, therepositories 420, 422, 424 and 426 may be implemented in the memory 452.In some embodiments, the modules of the system 400, namely the textgeneration module 402, the text receiving module 404, the graphconstructing module 406, the meeting partitioning module 410, thegraphical text analyzing module 412, the transition detecting module414, the advising module 416, the external information processing module418 may be combined or further partitioned. The modules of the system400 may be implemented in more than one physical machine in adistributed fashion.

FIG. 7 illustrates a process flow for facilitating a meeting of a groupof participants. In some embodiments, the system 400 performs theprocess flow shown in FIG. 7. At block 710, the system 400 generates agraph of words from the participants' speeches as the words are receivedfrom the participants. Specifically, the system 400 receives speechesfrom each participant in text or in audio. The system 400 converts anyspeech in audio into text in real time. The system 400 captures an orderin which the words are received from the participants in the graph.

At block 720, the system 400 partitions the group of participants of themeeting into a plurality of subgroups of participants. For instance, onesubgroup includes a teacher and another subgroup includes the studentslearning from the teacher in the meeting when the meeting takes place ina learning environment.

At block 730, the system 400 performs a graphical text analysis on thegraph to identify a cognitive state for each participant and a cognitivestate for each subgroup of participants. In some embodiments, the system400 also partitions the graph into a plurality of subgraphs that eachcorrespond to a subgroup of participants. The system 400 performs thegraphical text analysis on each subgraph.

At block 740, the system 400 identifies any transition from onecognitive state to another for each participant and each subgroup ofparticipants in order to detect any divergence of the cognitive statesby the participant or the subgroup during the meeting. In someembodiments, the system 400 determines whether a likelihood of thetransition is below or above a threshold. In response to determiningthat the likelihood of the transition is below the threshold, the system400 automatically gathers additional information other than the textreceived or converted at block 710 to increase the likelihood of thetransition using the gathered information. This additional informationincludes at least one of biometric information of a participant, socialnetwork content of a participant, and speeches made by a participant ina prior meeting.

At block 750, the system 400 informs at least one of the participants ofthe meeting about the identified cognitive state of a participant or asubgroup of participants. In some embodiments, the system 400automatically generates an advice based on the divergence detected atblock 740. The system 400 provides the advice to at least one of theparticipants. In some embodiments, the advice is provided in response todetermining at block 740 that the likelihood of the transition is abovethe threshold. The advice includes a suggestion to change a course ofthe meeting. For instance, the advice may include a suggestion for achange in lesson plans of the teacher based on the transitions incognitive state of the students when the meeting takes place in alearning environment.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A computer program product, comprising: a computer readable storagemedium having program instructions embodied therewith, the programinstructions readable by a processing circuit to cause the processingcircuit to perform a method of facilitating a meeting of a group ofparticipants, the method comprising: generating a graph of words fromspeeches of the participants, as the words are received from theparticipants; partitioning the group of participants into a plurality ofsubgroups of participants; performing a graphical text analysis on thegraph to identify a cognitive state for each participant and a cognitivestate for each subgroup of participants; and informing at least one ofthe participants about the identified cognitive state of a participantor a subgroup of participants.
 2. The computer program product of claim1, wherein the method further comprises: identifying any transition fromone cognitive state to another for each participant and each subgroup ofparticipants in order to detect any divergence of the cognitive statesby the participant or the subgroup during the meeting; automaticallygenerating an advice based on the detected divergence; and providing theadvice to at least one of the participants in order to influence acourse of the meeting.
 3. The computer program product of claim 2,wherein the method further comprises, in response to determining that alikelihood of the transition is below a threshold, automaticallygathering information other than the text to increase the likelihood ofthe transition using the gathered information, wherein the advice isprovided in response to determining that the likelihood of thetransition is above the threshold.
 4. The computer program product ofclaim 3, wherein the information other than the text includes at leastone of biometric information of a participant, social network content ofa participant, and speeches made by a participant in a prior meeting. 5.The computer program product of claim 2, wherein one subgroup includes ateacher and another subgroup includes students learning from the teacherin the meeting, wherein the advice includes a suggestion for a change inlesson plans of the teacher based on the transitions in cognitive stateof the students.
 6. The computer program product of claim 1, whereinperforming the graphical text analysis comprises: partitioning the graphinto a plurality of subgraphs that each correspond to a subgroup ofparticipants; and performing the graphical text analysis on eachsubgraph.
 7. The computer program product of claim 1, wherein generatingthe graph comprises: receiving the speeches from each participant intext or in audio; converting any speech in audio into text in real time;and capturing, in the graph, an order in which the words are receivedfrom the participants. 8.-14. (canceled)
 15. A computer system forfacilitating a meeting of a group of participants, the systemcomprising: a memory having computer readable instructions; and aprocessor configured to execute the computer readable instructions, theinstructions comprising: generating a graph of words from speeches ofthe participants, as the words are received from the participants;partitioning the group of participants into a plurality of subgroups ofparticipants; performing, by a computer, a graphical text analysis onthe graph to identify a cognitive state for each participant and acognitive state for each subgroup of participants; and informing atleast one of the participants about the identified cognitive state of aparticipant or a subgroup of participants.
 16. The computer system ofclaim 15, wherein the instructions further comprise: identifying anytransition from one cognitive state to another for each participant andeach subgroup of participants in order to detect any divergence of thecognitive states by the participant or the subgroup during the meeting;automatically generating an advice based on the detected divergence; andproviding the advice to at least one of the participants in order toinfluence a course of the meeting.
 17. The computer system of claim 16,wherein the instructions further comprise, in response to determiningthat a likelihood of the transition is below a threshold, automaticallygathering information other than the text to increase the likelihood ofthe transition using the gathered information, wherein the advice isprovided in response to determining that the likelihood of thetransition is above the threshold.
 18. The computer system of claim 17,wherein the information other than the text includes at least one ofbiometric information of a participant, social network content of aparticipant, and speeches made by a participant in a prior meeting. 19.The computer system of claim 15, wherein performing the graphical textanalysis comprises: partitioning the graph into a plurality of subgraphsthat each correspond to a subgroup of participants; and performing thegraphical text analysis on each subgraph.
 20. The computer system ofclaim 15, wherein generating the graph comprises: receiving the speechesfrom each participant in text or in audio; converting any speech inaudio into text in real time; and capturing, in the graph, an order inwhich the words are received from the participants.